Sherrillanderson3484

It is the purpose of this paper to justify the use of modulation equations for pattern forming systems in the case of multiple Turing instabilities with critical wave numbers having a ratio 12 by proving approximation results, presenting attractivity results, and discussing the existence of modulating fronts.Identification of multiple influential spreaders on complex networks is of great significance, which can help us speed up information diffusion and prevent disease from spreading to some extent. The traditional top-k strategy to solve an influence maximization problem based on node centrality is unsuitable for selecting several spreaders simultaneously because of influence overlapping. Besides, other heuristic methods have a poor ability to keep the balance between efficiency and computing time. In this paper, an efficient method is proposed to identify the decentralized influential spreaders on networks by edge percolation under the Susceptible-Infected-Recovered (SIR) model. Thanks to the average size of the connected component where one node is located under the edge percolation equivalent to the final spread range of this node under the SIR model approximately, it inspires us to choose suitable spreaders maximize the spread of influence. The experimental results show that our method has high efficiency compared with other benchmark methods on three synthetic networks and six empirical networks, and it also requires less time and cost.Modern view of network resilience and epidemic spreading has been shaped by percolation tools from statistical physics, where nodes and edges are removed or immunized randomly from a large-scale network. In this paper, we produce a theoretical framework for studying targeted immunization in networks, where only n nodes can be observed at a time with the most connected one among them being immunized and the immunity it has acquired may be lost subject to a decay probability ρ. We examine analytically the percolation properties as well as scaling laws, which uncover distinctive characters for Erdős-Rényi and power-law networks in the two dimensions of n and ρ. We study both the case of a fixed immunity loss rate as well as an asymptotic total loss scenario, paving the way to further understand temporary immunity in complex percolation processes with limited knowledge.In ecology, the intra- and inter-specific competition between individuals of mobile species for shared resources is mostly non-local; i.e., competition at any spatial position will not only be dependent on population at that position, but also on population in neighboring regions. Therefore, models that assume competition to be restricted to the individuals at that position only are actually oversimplifying a crucial physical process. For the past three decades, researchers have established the necessity of considering spatial non-locality while modeling ecological systems. Despite this ecological importance, studies incorporating this non-local nature of resource competition in an aquatic ecosystem are surprisingly scarce. To this end, the celebrated Scheffer's tri-trophic minimal model has been considered here as a base model due to its efficacy in describing the pelagic ecosystem with least complexity. It is modified into an integro-reaction-diffusion system to include the effect of non-local competition by introducing a weighted spatial average with a suitable influence function. A detailed analysis shows that the non-locality may have a destabilizing effect on underlying nutrient-plankton-fish dynamics. A local system in a stable equilibrium state can lose its stability through spatial Hopf and Turing bifurcations when strength of a non-local interaction is strong enough, which eventually generates a large range of spatial patterns. The relationship between a non-local interaction and fish predation has been established, which shows that fish predation contributes in damping of plankton oscillations. Thapsigargin Overall, results obtained here manifest the significance of non-locality in aquatic ecosystems and its possible contribution to the phenomena of "spatial patchiness."Percolation transition (PT) means the formation of a macroscopic-scale large cluster, which exhibits a continuous transition. However, when the growth of large clusters is globally suppressed, the type of PT is changed to a discontinuous transition for random networks. A question arises as to whether the type of PT is also changed for scale-free (SF) network, because the existence of hubs incites the formation of a giant cluster. Here, we apply a global suppression rule to the static model for SF networks and investigate properties of the PT. We find that even for SF networks with the degree exponent 2 less then λ less then 3, a hybrid PT occurs at a finite transition point tc, which we can control by the suppression strength. The order parameter jumps at tc - and exhibits a critical behavior at tc +.A sudden fall of stock prices happens during a pandemic due to the panic sell-off by the investors. Such a sell-off may continue for more than a day, leading to a significant crash in the stock price or, more specifically, an extreme event (EE). In this paper, Hilbert-Huang transformation and a structural break analysis (SBA) have been applied to identify and characterize an EE in the stock market due to the COVID-19 pandemic. The Hilbert spectrum shows a maximum energy concentration at the time of an EE, and hence, it is useful to identify such an event. The EE's significant energy concentration is more than four times the standard deviation above the mean energy of the normal fluctuation of stock prices. A statistical significance test for the intrinsic mode functions is applied, and the test found that the signal is not noisy. The degree of nonstationarity test shows that the indices and stock prices are nonstationary. We identify the time of influence of the EE on the stock price by using SBA. Furthermore, we have identified the time scale ( τ) of the shock and recovery of the stock price during the EE using the intrinsic mode function obtained from the empirical mode decomposition technique. The quality stocks with V-shape recovery during the COVID-19 pandemic have definite τ of shock and recovery, whereas the stressed stocks with L-shape recovery have no definite τ. The identification of τ of shock and recovery during an EE will help investors to differentiate between quality and stressed stocks. These studies will help investors to make appropriate investment decisions.